A reality check on the GARCH-MIDAS volatility models
本文用多种模型评估测试检验GARCH-MIDAS模型，发现标准检验可能误导，宏观经济变量对总方差预测的增益被高估，对长期方差预测的增益被低估，提醒研究者和从业者警惕数据挖掘偏差。
We employ a battery of model evaluation tests for a broad set of GARCH-MIDAS models and account for data snooping bias. We document that inferences based on standard tests for GM variance components can be misleading. Our data mining free results show that the gain of macro-variables in forecasting total (long-run) variance by GM models is overstated (understated). Estimation of different components of volatility is crucial for designing differentiated investing strategies, risk management plans and pricing derivative securities. Therefore, researchers and practitioners should be wary of data-mining bias, which may contaminate a forecast that may appear statistically validated using robust evaluation tests.